from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 1.969990 | 0.148898 | NaN | 0.000406 | 0.001970 | brute | -1 | 1 | 0.663 | 0.194904 | 0.006901 | 0.687 | 10.107465 | 10.113800 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 2.921108 | 0.028251 | NaN | 0.000274 | 0.002921 | brute | -1 | 5 | 0.757 | 0.194911 | 0.001272 | 0.742 | 14.986918 | 14.987238 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.210127 | 0.010942 | NaN | 0.000362 | 0.002210 | brute | 1 | 100 | 0.882 | 0.242505 | 0.003347 | 0.875 | 9.113756 | 9.114624 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.020651 | 0.000483 | NaN | 0.000039 | 0.020651 | brute | 1 | 100 | 1.000 | 0.008281 | 0.000219 | 0.000 | 2.493897 | 2.494773 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 2.907368 | 0.040011 | NaN | 0.000275 | 0.002907 | brute | -1 | 100 | 0.882 | 0.239126 | 0.001434 | 0.875 | 12.158308 | 12.158526 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.024246 | 0.002924 | NaN | 0.000033 | 0.024246 | brute | -1 | 100 | 1.000 | 0.008427 | 0.000831 | 0.000 | 2.877169 | 2.891140 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.184117 | 0.008282 | NaN | 0.000366 | 0.002184 | brute | 1 | 5 | 0.757 | 0.194345 | 0.001629 | 0.742 | 11.238362 | 11.238757 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.191104 | 0.003320 | NaN | 0.000672 | 0.001191 | brute | 1 | 1 | 0.663 | 0.194272 | 0.002326 | 0.687 | 6.131110 | 6.131550 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.730465 | 0.035290 | NaN | 0.000009 | 0.001730 | brute | -1 | 1 | 0.896 | 0.030833 | 0.000936 | 0.967 | 56.123240 | 56.149091 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.772486 | 0.027425 | NaN | 0.000006 | 0.002772 | brute | -1 | 5 | 0.922 | 0.031989 | 0.001134 | 0.974 | 86.669797 | 86.724230 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 2.101553 | 0.004865 | NaN | 0.000008 | 0.002102 | brute | 1 | 100 | 0.929 | 0.071073 | 0.002416 | 0.975 | 29.569097 | 29.586170 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.771628 | 0.027693 | NaN | 0.000006 | 0.002772 | brute | -1 | 100 | 0.929 | 0.070786 | 0.001922 | 0.975 | 39.154852 | 39.169284 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 2.079044 | 0.009464 | NaN | 0.000008 | 0.002079 | brute | 1 | 5 | 0.922 | 0.031891 | 0.000830 | 0.974 | 65.191640 | 65.213706 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.072272 | 0.005762 | NaN | 0.000015 | 0.001072 | brute | 1 | 1 | 0.896 | 0.030387 | 0.000222 | 0.967 | 35.287020 | 35.287958 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.953 | 0.0 | -1 | 1 | 0.049 | 0.004 | 0.233 | 0.233 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.891 | 0.0 | -1 | 5 | 0.048 | 0.000 | 0.240 | 0.240 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.903 | 0.0 | 1 | 100 | 0.049 | 0.000 | 0.239 | 0.239 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.851 | 0.0 | -1 | 100 | 0.049 | 0.000 | 0.239 | 0.239 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.892 | 0.0 | 1 | 5 | 0.048 | 0.000 | 0.242 | 0.242 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.900 | 0.0 | 1 | 1 | 0.048 | 0.000 | 0.239 | 0.239 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.330 | 0.0 | -1 | 1 | 0.009 | 0.000 | 0.522 | 0.522 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.326 | 0.0 | -1 | 5 | 0.009 | 0.000 | 0.524 | 0.524 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.326 | 0.0 | 1 | 100 | 0.009 | 0.000 | 0.525 | 0.525 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.328 | 0.0 | -1 | 100 | 0.010 | 0.000 | 0.511 | 0.511 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.001 | 0.306 | 0.0 | 1 | 5 | 0.009 | 0.000 | 0.568 | 0.568 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.328 | 0.0 | 1 | 1 | 0.009 | 0.000 | 0.514 | 0.515 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.970 | 0.149 | 0.000 | 0.002 | -1 | 1 | 0.195 | 0.007 | 10.107 | 10.114 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.022 | 0.002 | 0.000 | 0.022 | -1 | 1 | 0.008 | 0.000 | 2.743 | 2.744 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.921 | 0.028 | 0.000 | 0.003 | -1 | 5 | 0.195 | 0.001 | 14.987 | 14.987 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.002 | 0.000 | 0.024 | -1 | 5 | 0.008 | 0.000 | 2.885 | 2.886 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.210 | 0.011 | 0.000 | 0.002 | 1 | 100 | 0.243 | 0.003 | 9.114 | 9.115 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 100 | 0.008 | 0.000 | 2.494 | 2.495 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.907 | 0.040 | 0.000 | 0.003 | -1 | 100 | 0.239 | 0.001 | 12.158 | 12.159 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.003 | 0.000 | 0.024 | -1 | 100 | 0.008 | 0.001 | 2.877 | 2.891 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.184 | 0.008 | 0.000 | 0.002 | 1 | 5 | 0.194 | 0.002 | 11.238 | 11.239 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 5 | 0.008 | 0.000 | 2.545 | 2.545 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.191 | 0.003 | 0.001 | 0.001 | 1 | 1 | 0.194 | 0.002 | 6.131 | 6.132 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.019 | 0.000 | 0.000 | 0.019 | 1 | 1 | 0.008 | 0.000 | 2.329 | 2.330 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.730 | 0.035 | 0.000 | 0.002 | -1 | 1 | 0.031 | 0.001 | 56.123 | 56.149 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.004 | 0.000 | 0.006 | -1 | 1 | 0.001 | 0.000 | 7.693 | 7.740 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.772 | 0.027 | 0.000 | 0.003 | -1 | 5 | 0.032 | 0.001 | 86.670 | 86.724 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.007 | 0.003 | 0.000 | 0.007 | -1 | 5 | 0.001 | 0.000 | 9.477 | 9.562 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.102 | 0.005 | 0.000 | 0.002 | 1 | 100 | 0.071 | 0.002 | 29.569 | 29.586 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.914 | 3.932 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.772 | 0.028 | 0.000 | 0.003 | -1 | 100 | 0.071 | 0.002 | 39.155 | 39.169 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.002 | 0.000 | 0.006 | -1 | 100 | 0.001 | 0.000 | 7.469 | 7.509 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.079 | 0.009 | 0.000 | 0.002 | 1 | 5 | 0.032 | 0.001 | 65.192 | 65.214 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.400 | 4.414 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.072 | 0.006 | 0.000 | 0.001 | 1 | 1 | 0.030 | 0.000 | 35.287 | 35.288 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.658 | 2.675 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.787703 | 0.941130 | NaN | 0.000102 | 0.000788 | kd_tree | -1 | 1 | 0.929 | 0.107080 | 0.003512 | 0.910 | 7.356233 | 7.360189 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.068765 | 0.363824 | NaN | 0.000075 | 0.001069 | kd_tree | -1 | 5 | 0.946 | 0.189717 | 0.003287 | 0.941 | 5.633471 | 5.634316 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 5.509321 | 0.446831 | NaN | 0.000015 | 0.005509 | kd_tree | 1 | 100 | 0.951 | 0.576296 | 0.004887 | 0.940 | 9.559889 | 9.560233 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.285155 | 0.279967 | NaN | 0.000024 | 0.003285 | kd_tree | -1 | 100 | 0.951 | 0.573701 | 0.007064 | 0.940 | 5.726244 | 5.726678 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.701067 | 0.310033 | NaN | 0.000047 | 0.001701 | kd_tree | 1 | 5 | 0.946 | 0.195848 | 0.002602 | 0.941 | 8.685640 | 8.686406 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 0.934033 | 0.304402 | NaN | 0.000086 | 0.000934 | kd_tree | 1 | 1 | 0.929 | 0.106195 | 0.007010 | 0.910 | 8.795449 | 8.814591 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.027836 | 0.012823 | NaN | 0.000575 | 0.000028 | kd_tree | -1 | 1 | 0.891 | 0.000438 | 0.000045 | 0.879 | 63.521387 | 63.855657 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.024767 | 0.001410 | NaN | 0.000646 | 0.000025 | kd_tree | -1 | 5 | 0.911 | 0.000712 | 0.000024 | 0.905 | 34.784349 | 34.803503 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.037197 | 0.001582 | NaN | 0.000430 | 0.000037 | kd_tree | 1 | 100 | 0.894 | 0.005176 | 0.000126 | 0.917 | 7.186030 | 7.188153 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.042963 | 0.005407 | NaN | 0.000372 | 0.000043 | kd_tree | -1 | 100 | 0.894 | 0.006148 | 0.001438 | 0.917 | 6.988549 | 7.177246 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.021961 | 0.000242 | NaN | 0.000729 | 0.000022 | kd_tree | 1 | 5 | 0.911 | 0.000712 | 0.000047 | 0.905 | 30.838644 | 30.906630 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.020320 | 0.000222 | NaN | 0.000787 | 0.000020 | kd_tree | 1 | 1 | 0.891 | 0.000465 | 0.000045 | 0.879 | 43.675119 | 43.881440 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.205 | 0.027 | 0.025 | 0.0 | -1 | 1 | 0.784 | 0.059 | 4.090 | 4.101 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.899 | 0.040 | 0.021 | 0.0 | -1 | 5 | 0.763 | 0.017 | 5.113 | 5.114 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.940 | 0.064 | 0.020 | 0.0 | 1 | 100 | 0.742 | 0.005 | 5.313 | 5.313 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.893 | 0.059 | 0.021 | 0.0 | -1 | 100 | 0.777 | 0.016 | 5.010 | 5.012 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.848 | 0.074 | 0.021 | 0.0 | 1 | 5 | 0.747 | 0.010 | 5.148 | 5.148 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.901 | 0.050 | 0.021 | 0.0 | 1 | 1 | 0.782 | 0.065 | 4.988 | 5.005 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.017 | 0.0 | -1 | 1 | 0.003 | 0.002 | 0.269 | 0.326 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.025 | 0.0 | -1 | 5 | 0.001 | 0.001 | 0.445 | 0.581 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.027 | 0.0 | 1 | 100 | 0.001 | 0.001 | 0.490 | 0.592 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.026 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.637 | 0.637 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.027 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.653 | 0.654 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.027 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.617 | 0.617 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.788 | 0.941 | 0.000 | 0.001 | -1 | 1 | 0.107 | 0.004 | 7.356 | 7.360 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.539 | 9.974 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.069 | 0.364 | 0.000 | 0.001 | -1 | 5 | 0.190 | 0.003 | 5.633 | 5.634 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 7.458 | 7.777 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 5.509 | 0.447 | 0.000 | 0.006 | 1 | 100 | 0.576 | 0.005 | 9.560 | 9.560 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 4.044 | 4.155 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.285 | 0.280 | 0.000 | 0.003 | -1 | 100 | 0.574 | 0.007 | 5.726 | 5.727 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.097 | 6.325 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.701 | 0.310 | 0.000 | 0.002 | 1 | 5 | 0.196 | 0.003 | 8.686 | 8.686 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 0.000 | 0.000 | 3.906 | 4.035 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.934 | 0.304 | 0.000 | 0.001 | 1 | 1 | 0.106 | 0.007 | 8.795 | 8.815 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.945 | 4.160 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.028 | 0.013 | 0.001 | 0.000 | -1 | 1 | 0.000 | 0.000 | 63.521 | 63.856 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 28.412 | 30.072 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 34.784 | 34.804 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 26.323 | 27.415 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.037 | 0.002 | 0.000 | 0.000 | 1 | 100 | 0.005 | 0.000 | 7.186 | 7.188 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 6.722 | 7.039 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.043 | 0.005 | 0.000 | 0.000 | -1 | 100 | 0.006 | 0.001 | 6.989 | 7.177 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.001 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 22.651 | 23.394 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.022 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 30.839 | 30.907 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 6.923 | 7.269 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 43.675 | 43.881 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 7.025 | 7.328 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.575 | 0.069 | 30 | 0.028 | 0.0 | random | 0.438 | 0.032 | 1.311 | 1.315 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.640 | 0.016 | 30 | 0.025 | 0.0 | k-means++ | 0.470 | 0.032 | 1.363 | 1.366 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.897 | 0.221 | 30 | 0.136 | 0.0 | random | 2.717 | 0.037 | 2.170 | 2.170 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 6.070 | 0.012 | 30 | 0.132 | 0.0 | k-means++ | 2.857 | 0.014 | 2.125 | 2.125 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.000 | 30 | 0.009 | 0.000 | random | 0.0 | 0.0 | 7.221 | 13.132 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 9.048 | 13.674 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.009 | 0.000 | k-means++ | 0.0 | 0.0 | 12.384 | 14.092 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | k-means++ | 0.0 | 0.0 | 13.091 | 13.874 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.397 | 0.000 | random | 0.0 | 0.0 | 7.468 | 7.853 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.002 | 0.000 | 30 | 0.001 | 0.002 | random | 0.0 | 0.0 | 12.985 | 13.311 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.422 | 0.000 | k-means++ | 0.0 | 0.0 | 6.771 | 7.165 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | k-means++ | 0.0 | 0.0 | 12.284 | 12.613 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.002002 | 0.000107 | 20 | 0.007993 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000507 | 0.000062 | -0.000965 | 3.946182 | 3.975261 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.002085 | 0.000250 | 20 | 0.007673 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000492 | 0.000051 | -0.000750 | 4.235296 | 4.257751 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002795 | 0.000167 | 20 | 0.286235 | 0.000003 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.001122 | 0.000088 | 0.293767 | 2.490328 | 2.497911 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002932 | 0.000294 | 20 | 0.272867 | 0.000003 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.001121 | 0.000111 | 0.256968 | 2.614597 | 2.627344 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.086 | 0.001 | 20 | 0.002 | 0.0 | random | 0.030 | 0.001 | 2.824 | 2.826 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.246 | 0.003 | 20 | 0.001 | 0.0 | k-means++ | 0.094 | 0.001 | 2.621 | 2.621 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.221 | 0.003 | 20 | 0.036 | 0.0 | random | 0.124 | 0.003 | 1.774 | 1.774 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 0.658 | 0.012 | 20 | 0.012 | 0.0 | k-means++ | 0.356 | 0.002 | 1.848 | 1.848 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.008 | 0.000 | random | 0.001 | 0.0 | 3.946 | 3.975 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.0 | 20 | 0.000 | 0.002 | random | 0.000 | 0.0 | 13.969 | 14.375 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.008 | 0.000 | k-means++ | 0.000 | 0.0 | 4.235 | 4.258 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.0 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.0 | 13.791 | 14.235 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.286 | 0.000 | random | 0.001 | 0.0 | 2.490 | 2.498 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.0 | 20 | 0.000 | 0.002 | random | 0.000 | 0.0 | 10.664 | 10.819 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.273 | 0.000 | k-means++ | 0.001 | 0.0 | 2.615 | 2.627 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.0 | 20 | 0.001 | 0.002 | k-means++ | 0.000 | 0.0 | 9.480 | 9.601 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000393 | 0.000338 | [20] | 2.036946 | 3.927449e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000900 | 0.001705 | 0.55 | 0.436589 | 0.935433 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.001774 | 0.000169 | [26] | 4.509757 | 1.773932e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.005579 | 0.000721 | 0.28 | 0.317989 | 0.320634 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 11.311 | 0.401 | [20] | 0.071 | 0.000 | 1.937 | 0.031 | 5.840 | 5.840 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 0.912 | 0.504 | [26] | 0.088 | 0.001 | 0.794 | 0.024 | 1.149 | 1.149 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | [20] | 2.037 | 0.0 | 0.001 | 0.002 | 0.437 | 0.935 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | [20] | 0.014 | 0.0 | 0.000 | 0.000 | 0.375 | 0.387 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | [26] | 4.510 | 0.0 | 0.006 | 0.001 | 0.318 | 0.321 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | [26] | 1.049 | 0.0 | 0.002 | 0.000 | 0.044 | 0.044 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.009989 | 0.000153 | NaN | 8.008992 | 0.00001 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.016514 | 0.000188 | 0.122191 | 0.604867 | 0.604907 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.191 | 0.003 | 0.419 | 0.0 | 0.195 | 0.001 | 0.978 | 0.978 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.189 | 0.089 | 0.673 | 0.0 | 0.340 | 0.311 | 3.496 | 4.736 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.01 | 0.0 | 8.009 | 0.0 | 0.017 | 0.0 | 0.605 | 0.605 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.00 | 0.0 | 1.194 | 0.0 | 0.000 | 0.0 | 0.685 | 0.738 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.00 | 0.0 | 4.956 | 0.0 | 0.000 | 0.0 | 0.456 | 0.737 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.00 | 0.0 | 0.015 | 0.0 | 0.000 | 0.0 | 0.605 | 0.638 | See | See |